But the arms-length relationship between Machine Learning (ML) attacks and the law seem unlikely to continue. Despite the vulnerabilities that this community has demonstrated since 2004, as [1]} notes, ML is at the core of many critical systems including healthcare, defense, and finance . Given this, when such systems fail or get compromised, it seems inevitable that law and adversarial machine learning are on a crash course towards each other. But the relationship is under-theorized. In response to [2]} work on model stealing, one of the affected companies responded: “Said another way, even if stealing software were easy, there is still an important disincentive to do so in that it violates intellectual property law.” (see [3]}) Such a statement assumes, without proof, that model stealing can be sanctioned by existing intellectual property law. As we discuss below, it is entirety possible that it won't be. The goal of this paper is to begin to explore how for some attacks, existing law may provide protection for ML models, but in others, there may be less protection for machine learning systems than practitioners expect.
